通过计算机视觉模型和转移学习,通过X射线促进COVID的识别
Aparna S Varde1,2, Divydharshini Karthikeyan1, Weitian Wang1
1School of Computing, Montclair State University, Montclair, NJ USA.
Multimedia tools and applications
|June 26, 2023
概括
通过计算机视觉转移学习,使用最小的数据,从胸部X射线中准确识别COVID-19. 这种具有成本效益的方法提高了医疗保健中的诊断准确性和及时性.
科学领域:
- 医学成像分析分析 医学成像分析
- 计算机视觉在医疗保健中的应用
- 机器学习应用程序 机器学习应用程序
背景情况:
- 电子健康记录 (EHR) 越来越多地利用多媒体数据,包括复杂的医疗图像和视频.
- 准确有效地识别COVID-19对于患者管理和公共卫生至关重要.
- 现有的计算机视觉模型需要大量的数据进行培训,这对罕见或新出现的疾病构成挑战.
研究的目的:
- 通过使用胸部X射线检测COVID-19的计算机视觉转移学习的有效性.
- 确定最佳的计算机视觉模型和数据增强策略,以从有限的图像数据集中准确识别COVID-19.
- 用最少的培训和验证样本来实现最大的诊断准确度.
主要方法:
- 转移学习被应用于大量公开可用的胸部X射线数据集.
- 计算机视觉模型使用数据增强技术进行了调整.
- 通过调整培训和验证样本大小来评估模型性能,以找到准确性和效率的最佳参数.
主要成果:
- 转移学习有效地利用有限的胸部X射线数据来识别COVID-19.
- 数据增强策略提高了模型的适应性和性能.
- 该研究确定了最佳模型和样本大小,以实现COVID-19检测的高精度.
结论:
- 将胸部X光学与转移学习相结合,为提高COVID-19放射性解释的准确性和及时性提供了一种有希望的,具有成本效益的方法.
- 这种方法在COVID-19诊断和康复阶段有很大的应用潜力.
- 进一步的研究可以探索医疗保健中的高级多媒体分析和机器学习技术.
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